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Why ``xeofs``? | ||
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EOF analysis and related variants are typically based on a decomposition of a 2D matrix. | ||
When working with Earth observation data, however, the underlying structure | ||
of the data is often multi-dimensional, e.g. they can be described by spatial (longitude, latitude, height etc.), | ||
temporal (time, steps, lead times in forecasts etc.) and/or other (variable, sensor etc.) | ||
dimensions. ``xarray`` provides a great tool to handle these multi-dimensional | ||
data sets in Python. Performing EOF analysis and similar techniques using ``xarray`` | ||
could therefore speed up analysis by automatically taking care of dimension labels. | ||
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Although there exist already numerous Python packages for EOF analysis in ``xarray``, | ||
none did satify my personal needs which is why I tried to merge existing implementations and extend their funcionalities. | ||
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What can ``xeofs`` do for me? | ||
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* perform your analysis ``xarray`` (support for ``pandas`` and ``numpy`` is also provided) | ||
* analyze multi-dimensional data sets without having to keep track of all dimensions yourself | ||
* investigate the significance of your results | ||
* *planned, but not implemented yet*: work with large data sets with the help of ``dask`` | ||
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Supported methods | ||
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* EOF analysis / PCA | ||
* Varimax/Promax rotation for better interpretability | ||
* Maximum Covariance Analysis |